This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical...This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.展开更多
This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and ...This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.展开更多
Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance...Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance, it needs more complex image processing, and it will spend expensive computation cost. For the real time issue, this paper proposes edge gradient direction image registration applied to Computer Tomography(CT) image and Ultrasonography (US) image based on the cooperation of Graphic Processor Unit (GPU) and Central Processor Unit (CPU). GPU can significantly reduce the computation time. First, the CT image slice is extracted from the CT volume by the region growing and the interpolation algorithm. Secondly, the image pre-processing is employed to reduce the image noises and enhance the image features. There are two kinds of the image pre-processing algorithms invoked in this paper: 1) median filtering and 2) anisotropic diffusion. Last but not least, the image edge gradient information is obtained by Canny operator, and the similarity measurement based on gradient direction is employed to evaluate the similarity between the CT and the US images. The experimental results show that the proposed architecture can distinctively improve the efficiency and are more suitably applied to the real world.展开更多
文摘This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.
文摘This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.
文摘Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance, it needs more complex image processing, and it will spend expensive computation cost. For the real time issue, this paper proposes edge gradient direction image registration applied to Computer Tomography(CT) image and Ultrasonography (US) image based on the cooperation of Graphic Processor Unit (GPU) and Central Processor Unit (CPU). GPU can significantly reduce the computation time. First, the CT image slice is extracted from the CT volume by the region growing and the interpolation algorithm. Secondly, the image pre-processing is employed to reduce the image noises and enhance the image features. There are two kinds of the image pre-processing algorithms invoked in this paper: 1) median filtering and 2) anisotropic diffusion. Last but not least, the image edge gradient information is obtained by Canny operator, and the similarity measurement based on gradient direction is employed to evaluate the similarity between the CT and the US images. The experimental results show that the proposed architecture can distinctively improve the efficiency and are more suitably applied to the real world.
